Supervised learning and unsupervised learning are two main types of machine learning distinguished by their use of labeled versus unlabeled data. Supervised learning aims to predict outputs based on labeled training examples, while unsupervised learning seeks to discover patterns in unlabeled data. Typical applications include classification and regression for supervised learning, and clustering and dimensionality reduction for unsupervised learning.

1. Supervised learning and
unsupervised learning
Supervised learning and
unsupervised learning are two main
types of machine learning, each with
distinct characteristics and use cases.
Here's a breakdown of their
differences:

2. Data Labeling
•Supervised Learning: Uses labeled data,
meaning each training example is paired with an
output label. For example, in image classification,
each image (input) has a corresponding category
(output label).
•Unsupervised Learning: Uses unlabeled data,
meaning the algorithm must find patterns and
relationships on its own, without any specific
guidance on what to look for.

3. Goal
•Supervised Learning: The goal is to learn a
mapping function from input variables to
output variables, enabling the model to predict
outputs for new inputs. It's like learning with a
teacher.
•Unsupervised Learning: The goal is to
discover hidden patterns or intrinsic structures
in the data. It's like learning without a teacher.

4. Types of Problems
•Supervised Learning: Typical tasks include
classification (e.g., spam detection) and
regression (e.g., predicting house prices).
•Unsupervised Learning: Typical tasks
include clustering (e.g., customer
segmentation), dimensionality reduction
(e.g., Principal Component Analysis), and
association (e.g., market basket analysis).

5. Evaluation
1.Supervised Learning: Model
performance can be easily evaluated
using the labeled data (e.g., accuracy,
precision, recall, F1 score).
2.Unsupervised Learning: Evaluation is
more challenging due to the absence of
ground truth labels. Often, domain
experts must manually evaluate the
results.

6. Examples of Algorithms
•Supervised Learning: Linear Regression,
Logistic Regression, Decision Trees,
Random Forests, Support Vector Machines
(SVM), and Naive Bayes.
•Unsupervised Learning: K-Means
Clustering, Hierarchical Clustering,
DBSCAN, Principal Component Analysis
(PCA), and Autoencoders.